1. Field of the Invention
The present invention concerns a method to evaluate magnetic resonance spectroscopy data with a determination of the baseline superimposed on the resonance signals.
2. Description of the Prior Art
Magnetic resonance spectroscopy (MRS) has been used for more than four decades in physical, chemical and biochemical basic research, for example as an analysis technique or for structural clarification of complex molecules. Like magnetic resonance tomography, magnetic resonance spectroscopy (MR spectroscopy) is based on the principle of nuclear magnetic resonance. Imaging is not the goal of MR spectroscopy, but rather the generation of magnetic resonance spectra (MR spectra) for analysis of substances such as for a structure clarification. The resonant frequencies of isotopes that possess a magnetic moment (such as, for example, 1H, 13C, 19F or 31P) are dependent on the chemical environment of these isotopes. Thus the chemical structures in which the molecules are bound are mirrored in the respective resonant frequencies of the aforementioned isotopes. A determination of the resonant frequencies therefore enables differentiation between different materials. The signal intensity at the various resonant frequencies gives information about the concentration of the corresponding molecules.
If a molecule is brought into a basic magnetic field of a magnetic resonance apparatus, as occurs in spectroscopy, electrons of the molecule shield the nucleus of the molecule from the basic magnetic field. Due to this effect, the local magnetic field at the location of the nucleus changes by a few millionths of the external basic magnetic field. The associated change of the resonant frequency of this nucleus is called a chemical shift. Molecules thus can be identified using their chemical shift. The representation of MR signals in the frequency domain typically ensues as a curve of the signal intensity versus the chemical shift, wherein the chemical shift is given as a value relative to a reference signal and normalized to a reference signal. A dimensionless quantity thus is obtained, with smaller values designated in ppm (parts per million).
A resonance line of a nucleus may be split into a number of lines when further nuclei with a magnetic moment are located in the environment of the considered nucleus. The cause lies in what is know as the spin-spin coupling between the nuclei. The magnetic flux density of the basic magnetic field that a nucleus experiences thus depends not only on the electron shells around this nucleus but also on the orientation of the magnetic fields of the neighboring atoms.
Magnetic resonance spectroscopy using clinical magnetic resonance apparatuses is termed clinical magnetic resonance spectroscopy. In addition to MR imaging, MR spectroscopy provides information about the metabolic consistency of the examined tissue and also enables the in vivo examination of metabolic processes in people. In MR spectroscopy, many different metabolites are detectable, the existence and concentration of which can give information about neuronal functioning, metabolic changes and pathological changes in the brain, muscle tissue and other organs.
The techniques of localized magnetic resonance spectroscopy substantially differ from that of magnetic resonance imaging because, in addition to tomographic spatial resolution, the chemical shift also is resolved in spectroscopy. At the moment, two localization methods for magnetic resonance spectroscopy dominate in clinical application. One such method encompasses individual volume techniques based on echo methods, in which a spectrum of a previously selected target volume is registered. The other basic method encompasses spectroscopic imaging techniques, known as CSI methods (Chemical Shift Imaging) that simultaneously enable the recording of spectra of a number of spatially connected target volumes.
Spectroscopic examination methods are used both in clinical phosphor and proton spectroscopy. A three-dimensional CSI method includes, for example, the following steps: after a non-slice-selective 90° RF pulse, a combination of magnetic phase coding gradients of the three spatial directions is activated for a specific time, and after this the magnetic resonance signal in the presence of each gradient is read out. As mentioned, different combinations of phase coding gradients often are repeated until the desired spatial resolution is achieved. A four-dimensional Fourier transformation of the magnetic resonance signals provides the desired spatial distribution of the resonance lines. A two-dimensional CSI method results from the above-described three-dimensional CSI method, if the aforementioned, non-slice-selective RF pulse is replaced by a slice-selective excitation formed by a slice-selective RF pulse and a corresponding magnetic gradient, and a gradient in the phase coding direction is omitted.
The typically employed individual volume techniques are based on a detection of a stimulated echo or on a secondary spin echo. In both cases, a spatial resolution ensues by successive, selective excitations of three orthogonal slices. A target volume is defined by a slice volume of the aforementioned three slices. Only the magnetization of the target volume experiences all three selective RF pulses and thus contributes to the stimulated echo, or to the secondary spin echo. The spectrum of the target volume is obtained by one-dimensional Fourier transformation of a time signal corresponding to the stimulated echo or, respectively, the secondary spin echo.
The intensive water signals frequently are suppressed in clinical proton spectroscopy. This is known as water suppression and a method for water suppression is, for example, the CHESS technique, in which the nuclear spins of the water molecules are first selectively excited by narrow-band 90° RF pulses and their transverse magnetization is subsequently dephased by switched magnetic field gradients. Thus—in the ideal case—a detectable magnetization of the water molecules is no longer present in a directly subsequent spectroscopy method.
For the volume to be examined, a magnetic resonance signal is generated (for example with one of the described methods) that is acquired in the time domain and that is transformed by a Fourier transformation into an associated spectrum, with, for example, a real part absolute value of the spectrum being represented. The spectrum is characterized by resonance lines that are designated as peaks. These resonance lines or peaks appear mostly in the form of sharp, bell-shaped curves. Each of the resonance lines or peaks can thereby be associated with a maximum amplitude value that in turn determines an associated frequency value, and thus a chemical shift, of the resonance line that, for example, is characteristic for the resonance line and thus for a very specific substance located in the volume and emitting a magnetic resonance signal. Furthermore, an integral value for one of the resonance lines or peaks in an absorption spectrum gives information about the concentration of the associated substance in the examined volume.
It is ultimately the goal of the evaluation of a spectrum to identify, using the resonance lines, the substances in the examined volume, and to determine their concentration within the volume. This information should be optimally acquired in a fully automatic evaluation method and be presented for further interpretation to a viewer of the spectrum, for example a diagnosing physician.
This evaluation, particularly of clinical in-vivo magnetic resonance spectra, is aimed first at freeing the spectrum or its time signal from diverse artifacts such as frequency shifts, phase shifts and baseline distortions. Subsequently, an adaptation (fitting) of theoretical curves to the spectrum or its associated time signal is implemented to identify and quantify the substances, in particular the metabolites, in the examined volume. Each contribution of each of the metabolites must be described by a model function used to produce the theoretical curve, the specification of this function often ensuing in the time domain. The adaptation of the theoretical curves can, however, be implemented both in the time domain and in the frequency domain, but working in the time domain (particularly with regard to handling missing or erroneous measurement (data) points) has the advantages of a simpler model parameterization and higher speed, since the calculation burden for Fourier transformations back and forth, between time domain and the frequency domain is not present. In particular, in vivo spectra, which are acquired with short echo time, have (in addition to the typical metabolite signals) background signals arising from portions of remaining water, macromolecules and lipids. The sum of the background signals (to which, in addition to the water residue, lipid contaminations and macromolecules, overlapping resonances of metabolites with very low concentration contribute) is generally summarized under the term baseline. The contributions to the baseline are to a strong degree dependent on the type of the examined tissue and the acquisition technique used. The baseline therefore must be considered in the modeling of the metabolite signals.
The specification of the baseline ensues with a baseline model, also called a baseline model function. It can ensue in the frequency domain (frequency space) or in the time domain. In the frequency domain, smooth, slowly changing functions such as polynomials or splines, as well as wavelet representations, are conventionally used to specify or parameterize the baseline. Currently, Voigt lines or experimentally determined model spectra are used in the time domain. The parameterization by Voigt lines and model spectra is hereby limited to special applications, since each requires foreknowledge about the examined tissue. Full particulars for model formation or modeling of the baseline by means of wavelet representations and splines is described, for example, in the article by B. J. Soher et al. “Representation of Strong Baseline Contributions in 1H MR Spectra”, Magnetic Resonance in Medicine 45, 2001, pages 966 through 972.
Evaluation of an MR spectrum also can ensue on the basis of the data of the MR signal in the time domain without modeling the baseline. For this the first points of the magnetic resonance signal, which form the main portions of the baseline are not incorporated into the modeling of the metabolite signals. This method, however, leads to a worsening of the signal-to-noise ratio, meaning the signal-to-noise ratio of the modeled magnetic resonance signal portion drops and the adapted parameters exhibit a greater uncertainty. In the case of narrowly adjacent or strongly overlapping metabolite signals, this method actually breaks down entirely when an insufficient number points are omitted, since contributions of adjacent metabolites can mix, and the individual metabolite signals are overshadowed.